Reinforcement Learning-driven Intelligent Monitoring for Data Integrity in Smart Electricity Fee Channels
DOI:
https://doi.org/10.13052/jwe1540-9589.2474Keywords:
Web-based monitoring, reinforcement learning, anomaly detection, data integrity, smart energy systems, adaptive Web applicationsAbstract
Ensuring data integrity in Web-based electricity fee channels is increasingly challenging due to dynamic energy data, complex topologies, and the rigidity of static monitoring mechanisms. This paper introduces a novel RL-driven monitoring framework, embedded in a modular, standards-compliant Web architecture, that autonomously detects and mitigates data integrity issues in real time. The proposed framework integrates a deep Q-learning agent with semantic metadata pipelines and RESTful microservices to dynamically adjust detection thresholds, refine anomaly classification policies, and incorporate human feedback into its learning loop. Unlike conventional rule-based systems, the RL agent continuously refines its decision policy through real-time interaction with dynamic data streams and operator feedback. Extensive experiments conducted on emulated smart grid datasets demonstrate the system’s practical benefits: a 20% absolute increase in anomaly detection accuracy (from 75% to 95%), a 53% reduction in false positive rate (from 15% to 7%), and a stable average detection latency of 240 ms, all without human-in-the-loop reconfiguration. The RL agent also demonstrates stable convergence and linear scalability, making it well-suited for growing smart grid infrastructures. The system also incorporates a Web-native dashboard that visualizes time-aligned energy consumption and anomaly events while enabling real-time operator feedback, which further optimizes the learning trajectory. These results highlight the feasibility and effectiveness of embedding adaptive, self-optimizing learning agents directly into Web-based infrastructure to ensure long-term data integrity, transparency, and operational resilience. The proposed framework contributes to advancing intelligent Web engineering practices and lays the groundwork for scalable, autonomous monitoring solutions across a wide range of data-intensive infrastructure domains.
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References
Omitaomu, Olufemi A., and Haoran Niu. “Artificial intelligence techniques in smart grid: A survey.” Smart Cities 4, no. 2 (2021): 548–568.
Liu, X., Golab, L., Golab, W., Ilyas, I.F., Jin, S. “Smart Meter Data Analytics: Systems, Algorithms, and Benchmarking.” ACM Trans. Database Syst., 42(1), Article 2, 2017. https://doi.org/10.1145/3004295.
Khan, Abdullah Ayub, Asif Ali Laghari, Mamoon Rashid, Hang Li, Abdul Rehman Javed, and Thippa Reddy Gadekallu. “Artificial intelligence and blockchain technology for secure smart grid and power distribution Automation: A State-of-the-Art Review.” Sustainable Energy Technologies and Assessments 57 (2023): 103282.
Buksh, Zain, Neeraj A. Sharma, Rishal Chand, Jashnil Kumar, and A. B. M. Shawkat Ali. “Cybersecurity Challenges in Smart Grid IoT.” IoT for Smart Grid: Revolutionizing Electrical Engineering (2025): 175–206.
Mo, Y., Kim, T.H., Brancik, K., et al. “Cyber-Physical Security of Smart Grid Infrastructure.” Proc. IEEE, 100(1), 2012.
Li, Junlong, Chenghong Gu, Yue Xiang, and Furong Li. “Edge-cloud computing systems for smart grid: state-of-the-art, architecture, and applications.” Journal of Modern Power Systems and Clean Energy 10, no. 4 (2022): 805–817
Liu, J., Xiao, Y., Li, S., Liang, W., Chen, C.L.P. “Cyber Security and Privacy Issues in Smart Grids.” IEEE Commun. Surv. Tutor., 14(4), 2012, pp. 981–997. https://doi.org/10.1109/SURV.2011.122111.00145.
Moustafa, R., Shareef, H., Asna, M., Errouissi, R., Selvaraj, J. “A Smart Web-Based Power Quality and Energy Monitoring System With Enhanced Features.” IEEE Access, 13, 2025, pp. 88458–88471. https://doi.org/10.1109/ACCESS.2025.3571623.
Shahinzadeh, H., Moradi, J., Gharehpetian, G.B., et al. “IoT Architecture for Smart Grids.” IPAPS, 2019.
Eskandarnia, E., Al-Ammal, H., Ksantini, R., et al. “Deep Learning Techniques for Smart Meter Data Analytics: A Review.” SN Comput. Sci., 3(243), 2022. https://doi.org/10.1007/s42979-022-01161-6.
Ghasempour, A. “Internet of Things in Smart Grid: Architecture, Applications, Services, Key Technologies, and Challenges.” Inventions, 4(22), 2019. https://doi.org/10.3390/inventions4010022.
Fan, Z., Kulkarni, P., et al. “Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities.” IEEE Commun. Surv. Tutor., 15(1), 2013, pp. 21–38. https://doi.org/10.1109/SURV.2011.122211.00021.
Alulema, Darwin, Javier Criado, Luis Iribarne, Antonio Jesús Fernández-García, and Rosa Ayala. “A model-driven engineering approach for the service integration of IoT systems.” Cluster Computing 23, no. 3 (2020): 1937–1954.
Molina-Ríos, J., Pedreira-Souto, N. “Comparison of Development Methodologies in Web Applications.” Inf. Softw. Technol., 119, 2020, Article 106238.
Alatrash, Rawaa, Rojalina Priyadarshini, Hadi Ezaldeen, and Akram Alhinnawi. “A hybrid recommendation integrating semantic learner modelling and sentiment multi-classification.” Journal of Web Engineering 21, no. 4 (2022): 941–988.
Sutton, R.S., Barto, A.G. “Reinforcement Learning: An Introduction.” MIT Press, 2018.
Wang, S., et al. “Machine Learning in Network Anomaly Detection: A Survey.” IEEE Access, 9, 2021, pp. 152379–152396.
Escalona, M.J., Koch, N. “Requirements Engineering for Web Applications – A Comparative Study.” JWE, 2(3), 2004, pp. 193–212.
Palaniappan, S., et al. “Machine Learning Model for Predicting Net Environmental Effects.” J. Inform. Web Eng., 4(1), 2025, pp. 243–253.
Escalona, M. José, and Nora Koch. “Requirements engineering for web applications – a comparative study.” Journal of web Engineering (2003): 193–212.
Olsina, L., et al. “Web Application Evaluation and Refactoring: A Quality-Oriented Improvement Approach.” JWE, 7(4), 2008, pp. 258–280.
Kachergis, Emily, Scott W. Miller, Sarah E. McCord, Melissa Dickard, Shannon Savage, Lindsay V. Reynolds, Nika Lepak et al. “Adaptive monitoring for multiscale land management: Lessons learned from the Assessment, Inventory, and Monitoring (AIM) principles.” Rangelands 44, no. 1 (2022): 50–63.
Pfaff, M., Krcmar, H. “A Web-Based System Architecture for Ontology-Based Data Integration in IT Benchmarking.” Enterp. Inf. Syst., 12(3), 2018, pp. 236–258.
González-Mora, César, Irene Garrigós, Jose Zubcoff, and Jose-Norberto Mazón. “Model-based generation of web application programming interfaces to access open data.” Journal of Web Engineering 19, no. 7–8 (2020): 1147–1172.
